Overview

Dataset statistics

Number of variables39
Number of observations10324
Missing cells61109
Missing cells (%)15.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 MiB
Average record size in memory312.0 B

Variable types

Numeric8
Categorical31

Alerts

Unit of Measure (Per Pack) is highly correlated with Pack Price and 3 other fieldsHigh correlation
Line Item Quantity is highly correlated with Line Item Value and 1 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Pack Price is highly correlated with Unit of Measure (Per Pack)High correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 2 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_Direct Drop and 5 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Product Group_ACT is highly correlated with Sub Classification_ACTHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_ARV is highly correlated with Unit of Measure (Per Pack) and 4 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Unit of Measure (Per Pack) and 4 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_ACT is highly correlated with Product Group_ACTHigh correlation
Sub Classification_Adult is highly correlated with Product Group_ARV and 3 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Unit of Measure (Per Pack) and 4 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTM and 1 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_AdultHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Unit of Measure (Per Pack) is highly correlated with Product Group_ARV and 2 other fieldsHigh correlation
Line Item Quantity is highly correlated with Line Item Value and 1 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 2 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_Direct Drop and 5 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Product Group_ACT is highly correlated with Sub Classification_ACTHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_ARV is highly correlated with Unit of Measure (Per Pack) and 4 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Unit of Measure (Per Pack) and 4 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_ACT is highly correlated with Product Group_ACTHigh correlation
Sub Classification_Adult is highly correlated with Product Group_ARV and 3 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Unit of Measure (Per Pack) and 4 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTM and 1 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_AdultHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Unit of Measure (Per Pack) is highly correlated with Product Group_HRDTHigh correlation
Line Item Value is highly correlated with Line Item Insurance (USD)High correlation
Line Item Insurance (USD) is highly correlated with Line Item ValueHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 2 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_Direct Drop and 5 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Product Group_ACT is highly correlated with Sub Classification_ACTHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_ARV is highly correlated with Vendor INCO Term_EXW and 3 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Unit of Measure (Per Pack) and 4 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_ACT is highly correlated with Product Group_ACTHigh correlation
Sub Classification_Adult is highly correlated with Product Group_ARV and 3 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Vendor INCO Term_EXW and 3 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTM and 1 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_AdultHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Sub Classification_Adult and 3 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_AdultHigh correlation
Sub Classification_Adult is highly correlated with Sub Classification_HIV test and 3 other fieldsHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 2 other fieldsHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Sub Classification_ACT is highly correlated with Product Group_ACTHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTMHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_From RDC and 2 other fieldsHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_HRDT is highly correlated with Sub Classification_HIV test and 3 other fieldsHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Product Group_ARV is highly correlated with Sub Classification_HIV test and 3 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_From RDC and 5 other fieldsHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
Product Group_ACT is highly correlated with Sub Classification_ACTHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Unit of Measure (Per Pack) is highly correlated with Pack Price and 14 other fieldsHigh correlation
Line Item Quantity is highly correlated with Line Item Value and 2 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Pack Price is highly correlated with Unit of Measure (Per Pack) and 4 other fieldsHigh correlation
Unit Price is highly correlated with Pack Price and 6 other fieldsHigh correlation
Weight (Kilograms) is highly correlated with Line Item QuantityHigh correlation
Freight Cost (USD) is highly correlated with Unit PriceHigh correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Managed By_Ethiopia Field Office is highly correlated with Managed By_Haiti Field OfficeHigh correlation
Managed By_Haiti Field Office is highly correlated with Managed By_Ethiopia Field OfficeHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Unit of Measure (Per Pack) and 7 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Unit of Measure (Per Pack) and 7 other fieldsHigh correlation
Vendor INCO Term_DDP is highly correlated with Fulfill Via_Direct Drop and 3 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Unit of Measure (Per Pack) and 9 other fieldsHigh correlation
Vendor INCO Term_FCA is highly correlated with Unit of Measure (Per Pack)High correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Unit of Measure (Per Pack) and 7 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Vendor INCO Term_DDP and 2 other fieldsHigh correlation
Shipment Mode_Truck is highly correlated with Vendor INCO Term_EXW and 1 other fieldsHigh correlation
Product Group_ACT is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
Product Group_ANTM is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
Product Group_ARV is highly correlated with Unit of Measure (Per Pack) and 9 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Unit of Measure (Per Pack) and 9 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_ACT is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
Sub Classification_Adult is highly correlated with Unit of Measure (Per Pack) and 6 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Unit of Measure (Per Pack) and 9 other fieldsHigh correlation
Sub Classification_HIV test - Ancillary is highly correlated with Unit PriceHigh correlation
Sub Classification_Malaria is highly correlated with Unit of Measure (Per Pack) and 2 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Unit of Measure (Per Pack) has 7526 (72.9%) missing values Missing
Line Item Quantity has 7833 (75.9%) missing values Missing
Line Item Value has 7651 (74.1%) missing values Missing
Pack Price has 7591 (73.5%) missing values Missing
Unit Price has 8104 (78.5%) missing values Missing
Weight (Kilograms) has 7749 (75.1%) missing values Missing
Freight Cost (USD) has 6980 (67.6%) missing values Missing
Line Item Insurance (USD) has 7675 (74.3%) missing values Missing

Reproduction

Analysis started2022-05-21 12:09:12.116525
Analysis finished2022-05-21 12:09:32.877188
Duration20.76 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unit of Measure (Per Pack)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)0.6%
Missing7526
Missing (%)72.9%
Infinite0
Infinite (%)0.0%
Mean-0.387008864
Minimum-2.545008612
Maximum2.488270668
Zeros0
Zeros (%)0.0%
Negative1695
Negative (%)16.4%
Memory size80.8 KiB
2022-05-21T17:39:32.952986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2.545008612
5-th percentile-1.852619319
Q1-1.246828199
median-0.6003980502
Q30.7493681067
95-th percentile1.064433964
Maximum2.488270668
Range5.03327928
Interquartile range (IQR)1.996196306

Descriptive statistics

Standard deviation1.035151287
Coefficient of variation (CV)-2.674748264
Kurtosis-1.20284592
Mean-0.387008864
Median Absolute Deviation (MAD)0.6464301492
Skewness0.2581497963
Sum-1082.850801
Variance1.071538187
MonotonicityNot monotonic
2022-05-21T17:39:33.047775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
-1.246828199976
 
9.5%
0.7493681067670
 
6.5%
-0.6003980502474
 
4.6%
-1.852619319222
 
2.2%
1.064433964157
 
1.5%
0.465811242576
 
0.7%
0.286777643476
 
0.7%
0.919258362253
 
0.5%
1.21471044439
 
0.4%
-2.54500861222
 
0.2%
Other values (6)33
 
0.3%
(Missing)7526
72.9%
ValueCountFrequency (%)
-2.54500861222
 
0.2%
-1.852619319222
 
2.2%
-1.246828199976
9.5%
-0.6003980502474
4.6%
-0.14849174361
 
< 0.1%
0.068545847051
 
< 0.1%
0.16162640123
 
< 0.1%
0.286777643476
 
0.7%
0.465811242576
 
0.7%
0.7493681067670
6.5%
ValueCountFrequency (%)
2.48827066816
 
0.2%
2.1263180555
 
< 0.1%
1.7973001687
 
0.1%
1.21471044439
 
0.4%
1.064433964157
 
1.5%
0.919258362253
 
0.5%
0.7493681067670
6.5%
0.465811242576
 
0.7%
0.286777643476
 
0.7%
0.16162640123
 
< 0.1%

Line Item Quantity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1885
Distinct (%)75.7%
Missing7833
Missing (%)75.9%
Infinite0
Infinite (%)0.0%
Mean-0.5677200651
Minimum-7.737262175
Maximum2.709990041
Zeros0
Zeros (%)0.0%
Negative1552
Negative (%)15.0%
Memory size80.8 KiB
2022-05-21T17:39:33.167412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-7.737262175
5-th percentile-3.178408619
Q1-1.309615138
median-0.382846096
Q30.4004470332
95-th percentile1.371545261
Maximum2.709990041
Range10.44725222
Interquartile range (IQR)1.710062171

Descriptive statistics

Standard deviation1.404417711
Coefficient of variation (CV)-2.473785582
Kurtosis0.9393402194
Mean-0.5677200651
Median Absolute Deviation (MAD)0.850234331
Skewness-0.7968913738
Sum-1414.190682
Variance1.972389108
MonotonicityNot monotonic
2022-05-21T17:39:33.305044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.17840861967
 
0.6%
-0.234423388562
 
0.6%
0.712942493249
 
0.5%
-1.79247345927
 
0.3%
-1.23290899624
 
0.2%
-0.61390137322
 
0.2%
0.0400074058422
 
0.2%
-0.406268917818
 
0.2%
-0.876254582317
 
0.2%
1.19056633514
 
0.1%
Other values (1875)2169
 
21.0%
(Missing)7833
75.9%
ValueCountFrequency (%)
-7.7372621751
< 0.1%
-7.5310387361
< 0.1%
-6.3006576161
< 0.1%
-5.8571982511
< 0.1%
-5.8228498311
< 0.1%
-5.7815097181
< 0.1%
-5.5639951111
< 0.1%
-5.4357435921
< 0.1%
-5.37460161
< 0.1%
-5.3532634891
< 0.1%
ValueCountFrequency (%)
2.7099900411
 
< 0.1%
2.6777420921
 
< 0.1%
2.5960324641
 
< 0.1%
2.5182075123
< 0.1%
2.5172526961
 
< 0.1%
2.4009368781
 
< 0.1%
2.35450381
 
< 0.1%
2.3507235491
 
< 0.1%
2.259961341
 
< 0.1%
2.2548365142
< 0.1%

Line Item Value
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2338
Distinct (%)87.5%
Missing7651
Missing (%)74.1%
Infinite0
Infinite (%)0.0%
Mean-0.7554450409
Minimum-10.58742507
Maximum2.820319876
Zeros0
Zeros (%)0.0%
Negative1797
Negative (%)17.4%
Memory size80.8 KiB
2022-05-21T17:39:33.441678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-10.58742507
5-th percentile-3.34074962
Q1-1.588360726
median-0.6022750385
Q30.2477298968
95-th percentile1.27378983
Maximum2.820319876
Range13.40774495
Interquartile range (IQR)1.836090623

Descriptive statistics

Standard deviation1.440305998
Coefficient of variation (CV)-1.906566222
Kurtosis2.034045603
Mean-0.7554450409
Median Absolute Deviation (MAD)0.9110302595
Skewness-0.8120518696
Sum-2019.304594
Variance2.074481368
MonotonicityNot monotonic
2022-05-21T17:39:33.580307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.09896033929
 
0.3%
-1.38370683615
 
0.1%
-0.354037239910
 
0.1%
0.24885965179
 
0.1%
-1.0744764918
 
0.1%
-0.068755572087
 
0.1%
-2.8523919567
 
0.1%
-5.0010607967
 
0.1%
-2.5148291846
 
0.1%
-1.7196988946
 
0.1%
Other values (2328)2569
 
24.9%
(Missing)7651
74.1%
ValueCountFrequency (%)
-10.587425071
 
< 0.1%
-8.4920465623
< 0.1%
-7.9325003131
 
< 0.1%
-6.4263451191
 
< 0.1%
-6.3058537031
 
< 0.1%
-6.1585966871
 
< 0.1%
-6.0594736821
 
< 0.1%
-5.5747500311
 
< 0.1%
-5.4942815841
 
< 0.1%
-5.1647514981
 
< 0.1%
ValueCountFrequency (%)
2.8203198761
< 0.1%
2.7881755211
< 0.1%
2.7067437821
< 0.1%
2.6693621911
< 0.1%
2.6323848231
< 0.1%
2.4795860831
< 0.1%
2.467320011
< 0.1%
2.4131571061
< 0.1%
2.3918971281
< 0.1%
2.38421782
< 0.1%

Pack Price
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct382
Distinct (%)14.0%
Missing7591
Missing (%)73.5%
Infinite0
Infinite (%)0.0%
Mean-0.9302851245
Minimum-6.634310863
Maximum3.368069215
Zeros0
Zeros (%)0.0%
Negative1686
Negative (%)16.3%
Memory size80.8 KiB
2022-05-21T17:39:33.715944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-6.634310863
5-th percentile-3.408038335
Q1-1.636320608
median-0.8926659564
Q30.2413592752
95-th percentile0.6579009926
Maximum3.368069215
Range10.00238008
Interquartile range (IQR)1.877679883

Descriptive statistics

Standard deviation1.368560391
Coefficient of variation (CV)-1.471119289
Kurtosis0.4343237065
Mean-0.9302851245
Median Absolute Deviation (MAD)1.093605006
Skewness-0.4844509401
Sum-2542.469245
Variance1.872957544
MonotonicityNot monotonic
2022-05-21T17:39:33.845598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.511554379368
 
3.6%
0.2413592752307
 
3.0%
0.3854701662183
 
1.8%
-0.867294553684
 
0.8%
-1.63632060880
 
0.8%
-2.3591898776
 
0.7%
0.0523323785553
 
0.5%
-2.19875967547
 
0.5%
-2.88012796746
 
0.4%
0.0929046928744
 
0.4%
Other values (372)1445
 
14.0%
(Missing)7591
73.5%
ValueCountFrequency (%)
-6.6343108634
 
< 0.1%
-5.940771971
 
< 0.1%
-5.8606991373
 
< 0.1%
-5.7865653433
 
< 0.1%
-5.6529919931
 
< 0.1%
-5.5351763251
 
< 0.1%
-4.9914514091
 
< 0.1%
-4.5541839252
 
< 0.1%
-4.4935535451
 
< 0.1%
-4.40001926230
0.3%
ValueCountFrequency (%)
3.3680692151
 
< 0.1%
3.2930740551
 
< 0.1%
3.2869727153
 
< 0.1%
2.7706647631
 
< 0.1%
2.6991188361
 
< 0.1%
2.1149359159
 
0.1%
1.97307950139
0.4%
1.8366786453
 
< 0.1%
1.8321615933
 
< 0.1%
1.8132072021
 
< 0.1%

Unit Price
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct123
Distinct (%)5.5%
Missing8104
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean-2.120131245
Minimum-6.210905782
Maximum4.285930022
Zeros0
Zeros (%)0.0%
Negative2072
Negative (%)20.1%
Memory size80.8 KiB
2022-05-21T17:39:33.978242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-6.210905782
5-th percentile-4.058674359
Q1-2.865413854
median-2.471765919
Q3-1.200009787
95-th percentile0.2920545257
Maximum4.285930022
Range10.4968358
Interquartile range (IQR)1.665404067

Descriptive statistics

Standard deviation1.38008001
Coefficient of variation (CV)-0.6509408383
Kurtosis1.518567037
Mean-2.120131245
Median Absolute Deviation (MAD)0.9536304437
Skewness0.9128708615
Sum-4706.691365
Variance1.904620834
MonotonicityNot monotonic
2022-05-21T17:39:34.112882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.865413854411
 
4.0%
-1.200009787368
 
3.6%
-2.471765919193
 
1.9%
-3.425396363100
 
1.0%
-2.5085922677
 
0.7%
-2.97880120659
 
0.6%
-4.25317590658
 
0.6%
-3.63323324947
 
0.5%
-1.49223791647
 
0.5%
1.98682228646
 
0.4%
Other values (113)814
 
7.9%
(Missing)8104
78.5%
ValueCountFrequency (%)
-6.2109057826
 
0.1%
-5.2863265964
 
< 0.1%
-4.81426154314
 
0.1%
-4.49487220412
 
0.1%
-4.25317590658
0.6%
-4.05867435938
0.4%
-3.89591362843
0.4%
-3.75597310
 
0.1%
-3.63323324947
0.5%
-3.5239249753
 
< 0.1%
ValueCountFrequency (%)
4.2859300221
 
< 0.1%
2.5287002231
 
< 0.1%
2.4213533582
 
< 0.1%
2.1940436341
 
< 0.1%
2.0829444011
 
< 0.1%
2.0075384764
 
< 0.1%
2.0013685563
 
< 0.1%
1.98682228646
0.4%
1.92196715123
0.2%
1.6172644963
 
< 0.1%

Weight (Kilograms)
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1329
Distinct (%)51.6%
Missing7749
Missing (%)75.1%
Infinite0
Infinite (%)0.0%
Mean-1.089048401
Minimum-8.222078619
Maximum4.222255219
Zeros0
Zeros (%)0.0%
Negative2021
Negative (%)19.6%
Memory size80.8 KiB
2022-05-21T17:39:34.243533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-8.222078619
5-th percentile-3.668356144
Q1-1.857054632
median-0.8685950249
Q3-0.1557684829
95-th percentile0.9816339957
Maximum4.222255219
Range12.44433384
Interquartile range (IQR)1.701286149

Descriptive statistics

Standard deviation1.423681812
Coefficient of variation (CV)-1.307271385
Kurtosis1.136628643
Mean-1.089048401
Median Absolute Deviation (MAD)0.842265349
Skewness-0.6692143592
Sum-2804.299634
Variance2.026869901
MonotonicityNot monotonic
2022-05-21T17:39:34.382162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.651983167140
 
0.4%
-0.64853732321
 
0.2%
-0.97080931117
 
0.2%
-4.84064510514
 
0.1%
-0.0612339697213
 
0.1%
-2.03811132812
 
0.1%
-2.37916839911
 
0.1%
-0.650833232611
 
0.1%
-1.23961813111
 
0.1%
-0.651599708610
 
0.1%
Other values (1319)2415
 
23.4%
(Missing)7749
75.1%
ValueCountFrequency (%)
-8.2220786191
 
< 0.1%
-8.0833966821
 
< 0.1%
-7.5084865451
 
< 0.1%
-7.0491693431
 
< 0.1%
-6.735611091
 
< 0.1%
-6.5517335381
 
< 0.1%
-6.34965042
< 0.1%
-5.9420703612
< 0.1%
-5.854706933
< 0.1%
-5.8004359451
 
< 0.1%
ValueCountFrequency (%)
4.2222552191
 
< 0.1%
3.1322795811
 
< 0.1%
2.7778715051
 
< 0.1%
2.4874423831
 
< 0.1%
2.153299093
< 0.1%
1.9298187632
 
< 0.1%
1.9100727295
< 0.1%
1.9032919031
 
< 0.1%
1.889928922
 
< 0.1%
1.866123761
 
< 0.1%

Freight Cost (USD)
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1915
Distinct (%)57.3%
Missing6980
Missing (%)67.6%
Infinite0
Infinite (%)0.0%
Mean-0.8849097236
Minimum-9.377883548
Maximum2.786167865
Zeros0
Zeros (%)0.0%
Negative2447
Negative (%)23.7%
Memory size80.8 KiB
2022-05-21T17:39:34.516801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-9.377883548
5-th percentile-3.248062573
Q1-1.674583046
median-0.7285666091
Q30.05896032785
95-th percentile1.224052545
Maximum2.786167865
Range12.16405141
Interquartile range (IQR)1.733543374

Descriptive statistics

Standard deviation1.402958769
Coefficient of variation (CV)-1.585425871
Kurtosis0.5976375565
Mean-0.8849097236
Median Absolute Deviation (MAD)0.8807960691
Skewness-0.5073088447
Sum-2959.138116
Variance1.968293308
MonotonicityNot monotonic
2022-05-21T17:39:34.651459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.43463736918
 
0.2%
-0.538190209118
 
0.2%
-2.86936163816
 
0.2%
-1.19333076816
 
0.2%
-0.688582398915
 
0.1%
-1.30690640815
 
0.1%
-0.963147006214
 
0.1%
-0.250211830614
 
0.1%
-0.716210098714
 
0.1%
0.614184014313
 
0.1%
Other values (1905)3191
30.9%
(Missing)6980
67.6%
ValueCountFrequency (%)
-9.3778835481
 
< 0.1%
-6.8491976841
 
< 0.1%
-6.2323649581
 
< 0.1%
-6.0383403161
 
< 0.1%
-5.957381776
0.1%
-5.9508565911
 
< 0.1%
-5.6990109291
 
< 0.1%
-5.5943277561
 
< 0.1%
-5.4671133031
 
< 0.1%
-5.3753644311
 
< 0.1%
ValueCountFrequency (%)
2.7861678651
 
< 0.1%
2.5953716261
 
< 0.1%
2.3674136272
 
< 0.1%
2.1704937031
 
< 0.1%
2.1688300413
0.1%
2.1044289221
 
< 0.1%
2.0643542351
 
< 0.1%
2.0634957181
 
< 0.1%
2.0118773391
 
< 0.1%
1.9551573984
 
< 0.1%

Line Item Insurance (USD)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2379
Distinct (%)89.8%
Missing7675
Missing (%)74.3%
Infinite0
Infinite (%)0.0%
Mean-0.6593776372
Minimum-7.990451792
Maximum2.713955191
Zeros0
Zeros (%)0.0%
Negative1718
Negative (%)16.6%
Memory size80.8 KiB
2022-05-21T17:39:34.787120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-7.990451792
5-th percentile-3.204224957
Q1-1.525594673
median-0.5238464586
Q30.3364131205
95-th percentile1.348483084
Maximum2.713955191
Range10.70440698
Interquartile range (IQR)1.862007793

Descriptive statistics

Standard deviation1.397696899
Coefficient of variation (CV)-2.119721416
Kurtosis0.9656055371
Mean-0.6593776372
Median Absolute Deviation (MAD)0.9055838174
Skewness-0.6768163719
Sum-1746.691361
Variance1.95355662
MonotonicityNot monotonic
2022-05-21T17:39:34.925709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.55127263415
 
0.1%
-1.67688978613
 
0.1%
-1.79121150210
 
0.1%
-0.88922445178
 
0.1%
0.37908768148
 
0.1%
-1.9735144256
 
0.1%
0.25604066596
 
0.1%
0.18587650916
 
0.1%
-1.9515221526
 
0.1%
-0.65432420335
 
< 0.1%
Other values (2369)2566
 
24.9%
(Missing)7675
74.3%
ValueCountFrequency (%)
-7.9904517922
< 0.1%
-6.7195084221
< 0.1%
-6.1775137311
< 0.1%
-6.1678302331
< 0.1%
-5.9835339271
< 0.1%
-5.6814676151
< 0.1%
-5.6755598512
< 0.1%
-5.6408279631
< 0.1%
-5.6127786661
< 0.1%
-5.2894625691
< 0.1%
ValueCountFrequency (%)
2.7139551911
< 0.1%
2.6151394491
< 0.1%
2.4421253591
< 0.1%
2.3773786891
< 0.1%
2.3595707351
< 0.1%
2.3216415761
< 0.1%
2.3110378751
< 0.1%
2.2972206811
< 0.1%
2.2937037432
< 0.1%
2.2841011161
< 0.1%

Managed By_Ethiopia Field Office
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10323 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010323
> 99.9%
1.01
 
< 0.1%

Length

2022-05-21T17:39:35.162077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:35.225906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010323
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Managed By_Haiti Field Office
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10323 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010323
> 99.9%
1.01
 
< 0.1%

Length

2022-05-21T17:39:35.289735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:35.353562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010323
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Managed By_PMO - US
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1.0
10265 
0.0
 
59

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.010265
99.4%
0.059
 
0.6%

Length

2022-05-21T17:39:35.416394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:35.479226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.010265
99.4%
0.059
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Managed By_South Africa Field Office
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10267 
1.0
 
57

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010267
99.4%
1.057
 
0.6%

Length

2022-05-21T17:39:35.542058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:35.605887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010267
99.4%
1.057
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Fulfill Via_Direct Drop
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
5404 
1.0
4920 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.05404
52.3%
1.04920
47.7%

Length

2022-05-21T17:39:35.669717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:35.733588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.05404
52.3%
1.04920
47.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Fulfill Via_From RDC
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1.0
5404 
0.0
4920 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.05404
52.3%
0.04920
47.7%

Length

2022-05-21T17:39:35.797417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:35.860247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.05404
52.3%
0.04920
47.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10321 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010321
> 99.9%
1.03
 
< 0.1%

Length

2022-05-21T17:39:35.924075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:35.987903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010321
> 99.9%
1.03
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10049 
1.0
 
275

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010049
97.3%
1.0275
 
2.7%

Length

2022-05-21T17:39:36.050737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:36.114566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010049
97.3%
1.0275
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10315 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010315
99.9%
1.09
 
0.1%

Length

2022-05-21T17:39:36.177397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:36.243219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010315
99.9%
1.09
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_DDP
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
8881 
1.0
1443 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08881
86.0%
1.01443
 
14.0%

Length

2022-05-21T17:39:36.306053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:36.368883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08881
86.0%
1.01443
 
14.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10309 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010309
99.9%
1.015
 
0.1%

Length

2022-05-21T17:39:36.432713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:36.495546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010309
99.9%
1.015
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_EXW
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
7546 
1.0
2778 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.07546
73.1%
1.02778
 
26.9%

Length

2022-05-21T17:39:36.559337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:36.623204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07546
73.1%
1.02778
 
26.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_FCA
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
9927 
1.0
 
397

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09927
96.2%
1.0397
 
3.8%

Length

2022-05-21T17:39:36.686035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:36.749869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09927
96.2%
1.0397
 
3.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_N/A - From RDC
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1.0
5404 
0.0
4920 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.05404
52.3%
0.04920
47.7%

Length

2022-05-21T17:39:36.830651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:36.967284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.05404
52.3%
0.04920
47.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Shipment Mode_Air
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1.0
6113 
0.0
4211 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.06113
59.2%
0.04211
40.8%

Length

2022-05-21T17:39:37.104921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:37.241555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.06113
59.2%
0.04211
40.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
9674 
1.0
 
650

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09674
93.7%
1.0650
 
6.3%

Length

2022-05-21T17:39:37.544704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:37.687323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09674
93.7%
1.0650
 
6.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
9953 
1.0
 
371

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09953
96.4%
1.0371
 
3.6%

Length

2022-05-21T17:39:37.828944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:37.970565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09953
96.4%
1.0371
 
3.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Shipment Mode_Truck
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
7494 
1.0
2830 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07494
72.6%
1.02830
 
27.4%

Length

2022-05-21T17:39:38.112186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:38.255801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07494
72.6%
1.02830
 
27.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_ACT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10308 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010308
99.8%
1.016
 
0.2%

Length

2022-05-21T17:39:38.398421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:38.468230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010308
99.8%
1.016
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_ANTM
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10302 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010302
99.8%
1.022
 
0.2%

Length

2022-05-21T17:39:38.532059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:38.596885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010302
99.8%
1.022
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_ARV
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1.0
8550 
0.0
1774 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.08550
82.8%
0.01774
 
17.2%

Length

2022-05-21T17:39:38.660715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:38.725541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.08550
82.8%
0.01774
 
17.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_HRDT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
8596 
1.0
1728 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08596
83.3%
1.01728
 
16.7%

Length

2022-05-21T17:39:38.790368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:38.855194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08596
83.3%
1.01728
 
16.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_MRDT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10316 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010316
99.9%
1.08
 
0.1%

Length

2022-05-21T17:39:38.920021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:38.984847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010316
99.9%
1.08
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_ACT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10308 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010308
99.8%
1.016
 
0.2%

Length

2022-05-21T17:39:39.048676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:39.114500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010308
99.8%
1.016
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_Adult
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1.0
6595 
0.0
3729 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.06595
63.9%
0.03729
36.1%

Length

2022-05-21T17:39:39.178330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:39.243383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.06595
63.9%
0.03729
36.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_HIV test
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
8757 
1.0
1567 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08757
84.8%
1.01567
 
15.2%

Length

2022-05-21T17:39:39.306215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:39.370044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08757
84.8%
1.01567
 
15.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_HIV test - Ancillary
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10163 
1.0
 
161

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010163
98.4%
1.0161
 
1.6%

Length

2022-05-21T17:39:39.435870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:39.500696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010163
98.4%
1.0161
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_Malaria
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10294 
1.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010294
99.7%
1.030
 
0.3%

Length

2022-05-21T17:39:39.566521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:39.631347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010294
99.7%
1.030
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_Pediatric
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
8369 
1.0
1955 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08369
81.1%
1.01955
 
18.9%

Length

2022-05-21T17:39:39.696174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:39.761000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08369
81.1%
1.01955
 
18.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

First Line Designation_No
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
7030 
1.0
3294 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07030
68.1%
1.03294
31.9%

Length

2022-05-21T17:39:39.825827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:39.890653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07030
68.1%
1.03294
31.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

First Line Designation_Yes
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1.0
7030 
0.0
3294 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.07030
68.1%
0.03294
31.9%

Length

2022-05-21T17:39:40.075159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:39:40.139986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.07030
68.1%
0.03294
31.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-05-21T17:39:28.601629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:21.441736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:22.445090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:23.321759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:24.399874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:25.548799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:26.584030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:27.643197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:28.727293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:21.581362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:22.541831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:23.441437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:24.548477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:25.681447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:26.690743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:27.771850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:28.849965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:21.701081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:22.653532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:23.548153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:24.680130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:25.776191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:26.805437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:27.890532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:28.968648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:21.832727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:22.756257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:23.666834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:24.855668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:25.935802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:26.931100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:28.006223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:29.091320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:21.949378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:22.845019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:23.789506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:25.023206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:26.082371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:27.045795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:28.140862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:29.214989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:22.099019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:22.945713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:24.026871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:25.167818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:26.220003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:27.166470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:28.254559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:29.333671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:22.201742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:23.083344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:24.144557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:25.291488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:26.338688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:27.294128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:28.366261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:29.451356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:22.323377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:23.205073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:24.256258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:25.420143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:26.460359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:27.405832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:39:28.482948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-21T17:39:40.252684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-21T17:39:40.756336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-21T17:39:41.257994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-21T17:39:42.169559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-21T17:39:43.049201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-21T17:39:29.707710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-21T17:39:31.248548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-21T17:39:32.042424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-21T17:39:32.488231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unit of Measure (Per Pack)Line Item QuantityLine Item ValuePack PriceUnit PriceWeight (Kilograms)Freight Cost (USD)Line Item Insurance (USD)Managed By_Ethiopia Field OfficeManaged By_Haiti Field OfficeManaged By_PMO - USManaged By_South Africa Field OfficeFulfill Via_Direct DropFulfill Via_From RDCVendor INCO Term_CIFVendor INCO Term_CIPVendor INCO Term_DAPVendor INCO Term_DDPVendor INCO Term_DDUVendor INCO Term_EXWVendor INCO Term_FCAVendor INCO Term_N/A - From RDCShipment Mode_AirShipment Mode_Air CharterShipment Mode_OceanShipment Mode_TruckProduct Group_ACTProduct Group_ANTMProduct Group_ARVProduct Group_HRDTProduct Group_MRDTSub Classification_ACTSub Classification_AdultSub Classification_HIV testSub Classification_HIV test - AncillarySub Classification_MalariaSub Classification_PediatricFirst Line Designation_NoFirst Line Designation_Yes
0NaNNaNNaN-1.865678-2.217704NaNNaNNaN0.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.0
10.749368NaNNaNNaNNaNNaNNaNNaN0.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.01.0
2-1.246828NaNNaN0.241359-2.865414NaNNaNNaN0.00.01.00.01.00.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.0
3NaN-1.080566NaNNaNNaNNaN-1.427223NaN0.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
4NaN-0.710747NaNNaNNaN-1.325780.672544NaN0.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
50.749368NaNNaNNaNNaNNaNNaNNaN0.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.01.0
6-1.852619NaNNaN-1.472563NaNNaNNaNNaN0.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.01.0
7NaNNaNNaNNaNNaNNaNNaNNaN0.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
8NaNNaNNaNNaNNaNNaNNaNNaN0.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.0
9-0.600398NaNNaN-0.867295NaNNaNNaNNaN0.00.01.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0

Last rows

Unit of Measure (Per Pack)Line Item QuantityLine Item ValuePack PriceUnit PriceWeight (Kilograms)Freight Cost (USD)Line Item Insurance (USD)Managed By_Ethiopia Field OfficeManaged By_Haiti Field OfficeManaged By_PMO - USManaged By_South Africa Field OfficeFulfill Via_Direct DropFulfill Via_From RDCVendor INCO Term_CIFVendor INCO Term_CIPVendor INCO Term_DAPVendor INCO Term_DDPVendor INCO Term_DDUVendor INCO Term_EXWVendor INCO Term_FCAVendor INCO Term_N/A - From RDCShipment Mode_AirShipment Mode_Air CharterShipment Mode_OceanShipment Mode_TruckProduct Group_ACTProduct Group_ANTMProduct Group_ARVProduct Group_HRDTProduct Group_MRDTSub Classification_ACTSub Classification_AdultSub Classification_HIV testSub Classification_HIV test - AncillarySub Classification_MalariaSub Classification_PediatricFirst Line Designation_NoFirst Line Designation_Yes
10314NaNNaNNaNNaNNaN-1.819625-1.222445NaN0.00.01.00.00.01.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.00.00.01.01.00.0
10315-0.6003980.2551151.200701NaNNaN-0.135016-0.1815290.8017730.00.01.00.00.01.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
10316NaNNaNNaNNaNNaNNaNNaNNaN0.00.01.00.00.01.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
10317NaNNaNNaNNaNNaN-1.8764390.725691NaN0.00.01.00.00.01.00.00.00.00.00.00.00.01.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.0
10318NaN1.5409170.520780NaNNaN0.5466820.6920660.2444200.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.00.01.01.00.0
10319NaN1.3091090.246951NaNNaN0.5466820.692066-0.0555980.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.00.01.01.00.0
10320NaN-2.681951NaNNaNNaN-4.366331-1.797627NaN0.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.01.00.00.00.00.01.00.0
10321NaN2.5172532.669362NaNNaN1.5401161.1608762.3216420.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.01.00.00.00.00.01.00.0
10322NaNNaNNaNNaNNaNNaNNaNNaN0.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.01.00.00.00.00.00.01.0
10323NaN-0.782459NaNNaNNaN-0.343383NaNNaN0.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.00.01.01.00.0